


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


_snake_case			 =	logging.get_logger(__name__)

_snake_case			 =	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_snake_case			 =	{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

_snake_case			 =	{
    '''gpt2''': 10_24,
    '''gpt2-medium''': 10_24,
    '''gpt2-large''': 10_24,
    '''gpt2-xl''': 10_24,
    '''distilgpt2''': 10_24,
}
class       UpperCAmelCase_   (   UpperCAmelCase__       ):

   '''simple docstring'''




   __A					: Union[str, Any]							=						VOCAB_FILES_NAMES
   __A					: Optional[int]							=						PRETRAINED_VOCAB_FILES_MAP
   __A					: Optional[int]							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A					: Union[str, Any]							=						["input_ids", "attention_mask"]
   __A					: str							=						GPTaTokenizer






   def __init__(						self		,							__A=None		,							__A=None		,							__A=None		,							__A="<|endoftext|>"		,							__A="<|endoftext|>"		,							__A="<|endoftext|>"		,							__A=False		,							**__A		,							):






     """simple docstring"""





     super().__init__(
         __A		,							__A		,							tokenizer_file=__A		,							unk_token=__A		,							bos_token=__A		,							eos_token=__A		,							add_prefix_space=__A		,							**__A		,							)

     lowerCamelCase					:   Optional[Any]       =							kwargs.pop("add_bos_token"		,							__A					)

     lowerCamelCase					:   List[str]       =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
     if pre_tok_state.get("add_prefix_space"		,							__A					) != add_prefix_space:
       lowerCamelCase					:   List[Any]       =							getattr(__A		,							pre_tok_state.pop("type"					)					)
       lowerCamelCase					:   Optional[int]       =							add_prefix_space
       lowerCamelCase					:   List[Any]       =							pre_tok_class(**__A					)

     lowerCamelCase					:   List[Any]       =							add_prefix_space






   def _snake_case       (						self		,							*__A		,							**__A					):






     """simple docstring"""





     lowerCamelCase					:   Optional[int]       =							kwargs.get("is_split_into_words"		,							__A					)
     assert self.add_prefix_space or not is_split_into_words, (
         F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
         "to use it with pretokenized inputs."
     )

     return super()._batch_encode_plus(*__A		,							**__A					)






   def _snake_case       (						self		,							*__A		,							**__A					):






     """simple docstring"""





     lowerCamelCase					:   Union[str, Any]       =							kwargs.get("is_split_into_words"		,							__A					)

     assert self.add_prefix_space or not is_split_into_words, (
         F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
         "to use it with pretokenized inputs."
     )

     return super()._encode_plus(*__A		,							**__A					)






   def _snake_case       (						self		,							__A		,							__A = None					):






     """simple docstring"""





     lowerCamelCase					:   List[str]       =							self._tokenizer.model.save(__A		,							name=__A					)
     return tuple(__A					)






   def _snake_case       (						self		,							__A					):






     """simple docstring"""





     lowerCamelCase					:   str       =							[]
     for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(__A		,							add_special_tokens=__A					) + [self.eos_token_id]					)

     if len(__A					) > self.model_max_length:
       lowerCamelCase					:   Union[str, Any]       =							input_ids[-self.model_max_length :]
     return input_ids




'''simple docstring'''




from ....configuration_utils import PretrainedConfig
from ....utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Optional[Any]				="van"






     def __init__(					self						:						Union[str, Any]    ,							a						:						List[Any]=2_24    ,							a						:						Optional[int]=3    ,							a						:						Optional[Any]=[7, 3, 3, 3]    ,							a						:						int=[4, 2, 2, 2]    ,							a						:						Optional[int]=[64, 1_28, 3_20, 5_12]    ,							a						:						str=[3, 3, 12, 3]    ,							a						:						str=[8, 8, 4, 4]    ,							a						:						int="gelu"    ,							a						:						int=0.02    ,							a						:						Optional[int]=1e-6    ,							a						:						List[str]=1e-2    ,							a						:						Union[str, Any]=0.0    ,							a						:						int=0.0    ,							**a						:						Optional[Any]    ,							):





         """simple docstring"""



         super().__init__(**a       )
         __lowerCamelCase            =    image_size
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    patch_sizes
         __lowerCamelCase            =    strides
         __lowerCamelCase            =    hidden_sizes
         __lowerCamelCase            =    depths
         __lowerCamelCase            =    mlp_ratios
         __lowerCamelCase            =    hidden_act
         __lowerCamelCase            =    initializer_range
         __lowerCamelCase            =    layer_norm_eps
         __lowerCamelCase            =    layer_scale_init_value
         __lowerCamelCase            =    drop_path_rate
         __lowerCamelCase            =    dropout_rate
